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Deep Learning in Graph Domains for Sensorised Environments

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

In the future, robots and smart environments will cooperate to assist people, performing monitoring tasks to enhance people’s comfort and safety. These environments are characterised by a large number of sensors generating vast amounts of interrelated data that are naturally expressed as graphs. The predominant approach to deal with graph structures is two-stage. In the first stage, input data is transformed into vectors. In the second stage, vectors are processed to learn and extract conclusions. Unfortunately, valuable information is generally lost in the graph-to-vector conversion. Techniques natively working with graph-representations are a less explored approach. This PhD will focus on enabling robots and environment sensors to share a common graph-like world model representation and to perform reasoning tasks using machine learning techniques specially designed to work with graph structures.
Collaborator: Ortelio Ltd.

Further information:

Funding Notes

This studentship provides a maintenance allowance (currently £14,777 per year) and covers tuition fees at the home/EU level. Applicants from outside the EU are welcome but they will need to pay the difference between the home/EU and international fee levels; that difference is currently £12,290/year.

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